AI-Augmented Developer – Advanced
The 'AI-Augmented Developer, Advanced' course is the follow-up to the 'AI-Augmented Developer' course, intended for those who wish to deepen their collaboration with artificial intelligence agents and the automation of complex processes. Participants learn to orchestrate several specialized agents capable of coordinating, delegating tasks, and continuously improving code quality. They also discover how to transform functional specifications into automated implementations through a specs-oriented approach ensuring consistency and reliability. The training addresses the creation of MCP servers from conception to production, covering configuration and security aspects. This program marks a key step towards complete expertise in augmented development, where humans and artificial intelligence collaborate fluidly and efficiently to create the software of tomorrow.
What you will learn
- Design and orchestrate multi-agent systems capable of collaborating effectively to accomplish complex tasks.
- Write and use technical specifications to enable the automatic generation of coherent and maintainable code.
- Create, configure, and secure a functional MCP server.
Prerequisites
- Practical mastery of at least one programming language (Python, JavaScript, Java, C#, TypeScript, Go...).
- Daily experience with Git and the use of a modern IDE (VS Code, IntelliJ, WebStorm, etc.).
- Possess basic skills in command line and file editing.
- Experience with teamwork: code review, collaborative workflows.
- A sensitivity to generative AI and prompt engineering is an asset to maximize the benefits of the training.
Target audience
- Software Engineer (operational backend/frontend developer.s), Software Architect, Tech Leader in companies, IT service companies, startups, and scale-ups who wish to boost their efficiency with AI while maintaining a high level of code quality., Teams concerned with maintainability, robustness, and best practices, who are looking to use AI to modernize their methods while improving the quality of their deliverables.
Training Program
3 modules to master the fundamentals
Topics covered
- →Multi-agent architecture: orchestration and collaboration / meta agent & sub-agents
- →Depth of reasoning and specialized agents
- →Delegation of complex tasks
- →Effective orchestration patterns
- →Error handling and continuous quality improvement
- →Monitoring of generated code quality
Activities
Feedback following the workshop on Module 7
Agent orchestration: pipeline architecture → implementation → tests → documentation
Automatic multi-criteria validation system (performance, security, maintainability)
Collaborative debugging with specialized quality agents
Topics covered
- →Principles of specification-oriented development
- →GitHub Spec-Kit: presentation and practical use
- →Writing specifications usable by AI
- →From requirements specification to automated implementation
- →Maintaining spec/code consistency
Activities
Spec-Kit Installation
Writing a spec with Spec-Kit and automated implementation
Topics covered
- →Architecture of an MCP server
- →MCP reminder: client/server, resources, tools, prompts
- →Structure and lifecycle of a server
- →Official SDK (TypeScript/Python)
- →Security of your server
- →Configuration and deployment
Activities
Create and test an MCP server
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote